A Method of Improved Support Vector Machine for Network Security Situation Forecasting

Abstract:

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Aiming at the problem that parameters of Support Vector Machines (SVM) are very difficult to confirm, this paper points out a parameter selection method for SVM based on Particle Swarm Optimization (PSO), which can make the SVM more scientific and reasonable in parameters selection; and thus enhance the forecast accuracy of the network security situation. The Simulation results show that the optimized SVR forecast model has good forecast accuracy for the network security situation, and present the future changing at a macro level, then help the network managers control network.

Info:

Periodical:

Edited by:

Yanwen Wu

Pages:

291-296

DOI:

10.4028/www.scientific.net/AMR.187.291

Citation:

Y. C. Li and J. T. Jing, "A Method of Improved Support Vector Machine for Network Security Situation Forecasting", Advanced Materials Research, Vol. 187, pp. 291-296, 2011

Online since:

February 2011

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Price:

$35.00

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